Non-small-cell lung cancer prediction using radiomic features and machine learning methods

• Published in: International journal of computers & applications Vol. 44; no. 12; pp. 1161 - 1169
• Main Authors: Shanthi, S., Rajkumar, N.
• Format: Journal Article
• Language: English
• Published: Calgary Taylor & Francis 02.12.2022; Taylor & Francis Ltd
• Subjects: Algorithms; Classification; correlation-based feature selection (CFS); Data analysis; Feature extraction; Gabor filters; gray-level co-occurrence matrix (GLCM); Lung cancer; Machine learning; minimum redundancy maximum relevance (MRMR); Naïve Bayes and decision tree; non-small-cell lung cancer (NSCLA); Radiomics
• ISSN: 1206-212X; 1925-7074
• DOI: 10.1080/1206212X.2019.1693723

One of the primary causes of deaths related to cancer all over the world is Lung cancer. The history of the patient and his histological classification in terms of lung cancer has provided critical information regarding the characteristics of tissues and anatomical locations. There are many different studies that have depicted the radiomic features and their power of prediction in the detection of lung cancer. But its quantitative size in terms of data is large and has been resulting in major challenges in the algorithms of classification. In order to overcome this, symbolic approach to data analysis which employs many different quantitative data is proposed. The work further investigates different techniques of feature selection in order to predict the histologic subtypes of lung cancer by using either symbolic data or the radiomic features. These features have been extracted by using a gray-level co-occurrence matrix (GLCM), the Gabor filter and the fusion that was achieved by making use of concatenation once there is a normalization of the Z score. The results of the experiment have proved that the proposed method had a better performance compared to the other methods.